Noising methods for a clique partitioning problem

Authors:
Irène Charon;Olivier Hudry
Affiliations:
Éole nationale supérieure des télécommunications, Paris cedex, France;Éole nationale supérieure des télécommunications, Paris cedex, France
Venue:
Discrete Applied Mathematics - Special issue: IV ALIO/EURO workshop on applied combinatorial optimization
Year:
2006

Citing 10
Cited 1

NP-hard problems in hierarchical-tree clustering

Acta Informatica
Threshold accepting: a general purpose optimization algorithm appearing superior to simulated annealing

Journal of Computational Physics
Modern heuristic techniques for combinatorial problems

Modern heuristic techniques for combinatorial problems
Simulated annealing

Modern heuristic techniques for combinatorial problems
Cluster analysis and mathematical programming

Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
A modified noising algorithm for the graph partitioning problem

Integration, the VLSI Journal
Meta-Heuristics: Theory and Applications

Meta-Heuristics: Theory and Applications
Local Search in Combinatorial Optimization

Local Search in Combinatorial Optimization
Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization

Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization
Essays and Surveys in Metaheuristics

Essays and Surveys in Metaheuristics

A tabu search approach for the minimum sum-of-squares clustering problem

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper deals with the application of noising methods to a clique partitioning problem for a weighted graph. The aim is to study different ways to add noise to the data, and to show that the choice of the noise-adding-scheme may have some impact on the performance of these methods. Among the noise-adding-schemes described here, two of them are totally new, leading to the "forgotten vertices" and to the "forgotten edges" methods. We also experimentally study a generic noising method that automatically tunes its parameters. For each noise-adding-scheme, we compare a variant which inserts descents and a variant which does not.